1. Technical Field
One or more embodiments of the present invention relate generally to systems and methods for patch-based image enhancement. More specifically, one or more embodiments of the present invention relate to systems and methods of creating and using a discriminative index tree for patch-based image enhancement.
2. Background and Relevant Art
Image enhancing techniques (e.g., deblurring, denoising, enlarging, restoring, superresolution) often involve solving a severely ill-posed problem. Conventional techniques often use image priors to aid in converging to a reasonable result. The use of image priors allows image enhancement systems to estimate visual content from available content in an image and/or other information to enhance the image. For example, an enhancement process can use information obtained from a blurry image (e.g., pixel information, color information, etc.) and statistical distribution information from image priors to deblur the blurry image.
The complexity of many types of image enhancement processes often introduces tradeoffs into the processing schemes. Some methods of image enhancement require less processing power than other methods, but may not be able to consistently and accurately provide quality results. For example, some image enhancement methods use gradient filters, which assume that the magnitude of image gradients follows certain distributions. Gradient filters can be fast, but may be unable to consistently provide high quality results.
To address the shortcomings of gradient filter approaches, some image enhancement systems use image priors with larger spatial support. For example, one such method formulates image restoration problems within a Conditional Random Field (“CRF”) framework, which associates nonadjacent pixels by connecting them in the field. Conventional CRF methods can provide larger spatial support than gradient priors, but can also suffer from optimization tractability. While some CRF methods also adopt approximate inference to improve optimization, the processing speed of such CRF methods is often still slow.
A more recent trend is to define probabilistic priors on image patches (i.e., probabilistic patch-based priors). One such technique is Expected Patch Log-Likelihood (“EPLL”). Conventional EPLL-based processes train statistical data using a large number of natural or sharp image patches to compute Gaussians. Traditional EPLL-based processes takes image patches and compares them to all of the Gaussians to determine the mostly probable Gaussian to which a particular image patch corresponds. The conventional EPLL then pushes the image patch toward the Gaussian to make the image patch look like the training data (e.g., a sharp image). This process is repeated multiple times to obtain a final enhanced image.
While conventional probabilistic patch-based methods, such as the EPLL process, tend to be more accurate than gradient-based methods, probabilistic patch-based methods typically suffer from longer processing times due to the number of calculations required. For example, it is not uncommon for an EPLL-based non-blind deblurring process to take tens of minutes for a megapixel image. A blind EPLL-based deblurring process can take two to three hours due to the intensive computations. Specifically, the conventional EPLL processes typically compares over 200 Gaussians each with 64 dimensions to each patch of the image. As such, the conventional EPLL process can have millions of factors to compute as part of the enhancement process that create a bottleneck that leads to long processing times.
These and other disadvantages may exist with respect to conventional image enhancement techniques.